In the last decades, Natural Language Processing (NLP) has been equally hyped and criticized. All in all, many applications emerged in the real world following intense and continued research and development. Here’s a list of the most prominent success stories.
Given that this blog is about named entity recognition (NER), itself an NLP application, we would be biased at including NER to this list. As such, we’ve excluded ourselves from the chart-toppers ;)
#5: Chat bots
"HELLO, MY NAME IS DOCTOR SBAITSO.
I AM HERE TO HELP YOU. SAY WHATEVER IS ON YOUR MIND FREELY, OUR CONVERSATION WILL BE KEPT IN THE STRICTEST CONFIDENCE. MEMORY CONTENTS WILL BE WIPED CLEAN AFTER YOU LEAVE,
SO, TELL ME ABOUT YOUR PROBLEMS."
The first time I chatted with Dr. Sbaitso, I was about 12 years old. Probably more than anything else, it has influenced my career path. Since then, chat bots such as ELIZA, A.L.I.C.E. and Jabberwacky propelled the art of conversational robots, leading to Automated Service Agent applications (see NextIT)
For its lasting impact on generations of NLP developers, and for the interesting improvements that ensued, Chat bots rank #5.
#4: NLP-based search engines
Ask Jeeves pioneered it, Powerset redefined it, but we are all somewhat skeptical when it comes to beating Google’s classic vector space models and ranking techniques., Do we really need shallow NLP parsing to answer “When did Einstein die,” or will statistical fact extraction suffice?
Though it is the Holy Grail of NLPers, it has not yet surpassed current information retrieval techniques. As such, NLP-based search engines rank #4.
#3: Speech recognition
Microsoft and Ford just teamed up to develop in-car speech recognition. But they forgot to include Electronic Voice Alert, a feature of mid-80s luxury Chrysler cars!
In all seriousness, automatic speech recognition (ASR) is a vital application for hand-free computing (for disabled persons or for certain circumstances, such as driving), and transcription. It is also poised to revolutionize audio-video content retrieval.
For where it came from, and for where it’s going, ASR ranks #3.
#2: Machine translation
“It is apparent to me that the possibilities of the aeroplane, which two or three years ago were thought to hold the solution to the [flying machine] problem, have been exhausted, and that we must turn elsewhere.” – Thomas Edison, inventor, 1895
The “heavier-than-air” problem that once plagued flight technology is probably the best comparison we can make to AI and machine translation (MT). It was long believed that MT would require a completely automatic understanding of human language before a resolution finally came. But today’s Google and Government of Canada systems surpass human translation abilities (can you translate from French to Chinese? Not me.) Their good level of precision makes them useful in many applications.
People are constantly pinpointing these systems’ shortcomings, but nobody would contest their second-place ranking on this list.
#1: Knowledge discovery in texts
Have you ever heard of software that finds new relationships and interactions between genes, proteins or cells? By mining large collections of scientific literature, NLP agents can discover and highlight novel and surprising knowledge.
What makes knowledge discovery so promising is the hope that, in the near future, we may monitor all these documents that are just too abundant to be processed manually. Early forms of knowledge discovery, such as data mining, are already used for Business Intelligence (BI) and outside the NLP world, examples of machine-made inventions already exist.
As a form of technological singularity, and as an emerging field of research for NLP, knowledge discovery gets first place on this list of top NLP applications.
YooName - named entity recognition 